Assessing the combined performance of texture and morphological parameters in distinguishing breast tumors in ultrasound images




This work aims to investigate the combination of morphological and texture parameters in distinguishing between malignant and benign breast tumors in ultrasound images.


Linear discriminant analysis was applied to sets of up to five parameters, and then the performances were assessed using the areaAz (± standard error) under the receiver operator characteristic curve, accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value, and negative predictive value.


The most relevant individual parameter was the normalized residual value (nrv), calculated from the convex polygon technique. The best performance among all studied combinations was achieved by two morphological and three texture parameters (nrv, con, std, R, and asmi), which correctly distinguished nearly 85% of the breast tumors.


This result indicates that the combination of morphological and texture parameters may be useful to assist physicians in the diagnostic process, especially if it is associated with an automatic classification tool.